A citation-based method for searching scientific literature

Peter V Kharchenko, Lev Silberstein, David T Scadden. Nat Methods 2014
Times Cited: 584







List of co-cited articles
920 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, Rahul Satija. Nat Biotechnol 2018
41

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
Cole Trapnell, Davide Cacchiarelli, Jonna Grimsby, Prapti Pokharel, Shuqiang Li, Michael Morse, Niall J Lennon, Kenneth J Livak, Tarjei S Mikkelsen, John L Rinn. Nat Biotechnol 2014
33

Comprehensive Integration of Single-Cell Data.
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, Rahul Satija. Cell 2019
33

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
Michael I Love, Wolfgang Huber, Simon Anders. Genome Biol 2014
32

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.
Evan Z Macosko, Anindita Basu, Rahul Satija, James Nemesh, Karthik Shekhar, Melissa Goldman, Itay Tirosh, Allison R Bialas, Nolan Kamitaki, Emily M Martersteck,[...]. Cell 2015
27

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
Greg Finak, Andrew McDavid, Masanao Yajima, Jingyuan Deng, Vivian Gersuk, Alex K Shalek, Chloe K Slichter, Hannah W Miller, M Juliana McElrath, Martin Prlic,[...]. Genome Biol 2015
766
26


Massively parallel digital transcriptional profiling of single cells.
Grace X Y Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo B Ziraldo, Tobias D Wheeler, Geoff P McDermott, Junjie Zhu,[...]. Nat Commun 2017
26

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.
David van Dijk, Roshan Sharma, Juozas Nainys, Kristina Yim, Pooja Kathail, Ambrose J Carr, Cassandra Burdziak, Kevin R Moon, Christine L Chaffer, Diwakar Pattabiraman,[...]. Cell 2018
431
26

SAVER: gene expression recovery for single-cell RNA sequencing.
Mo Huang, Jingshu Wang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, Roberto Bonasio, John I Murray, Arjun Raj, Mingyao Li, Nancy R Zhang. Nat Methods 2018
212
25

Challenges in unsupervised clustering of single-cell RNA-seq data.
Vladimir Yu Kiselev, Tallulah S Andrews, Martin Hemberg. Nat Rev Genet 2019
271
23

Current best practices in single-cell RNA-seq analysis: a tutorial.
Malte D Luecken, Fabian J Theis. Mol Syst Biol 2019
419
22

SC3: consensus clustering of single-cell RNA-seq data.
Vladimir Yu Kiselev, Kristina Kirschner, Michael T Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar N Natarajan, Wolf Reik, Mauricio Barahona, Anthony R Green,[...]. Nat Methods 2017
527
21

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.
Allon M Klein, Linas Mazutis, Ilke Akartuna, Naren Tallapragada, Adrian Veres, Victor Li, Leonid Peshkin, David A Weitz, Marc W Kirschner. Cell 2015
21

mRNA-Seq whole-transcriptome analysis of a single cell.
Fuchou Tang, Catalin Barbacioru, Yangzhou Wang, Ellen Nordman, Clarence Lee, Nanlan Xu, Xiaohui Wang, John Bodeau, Brian B Tuch, Asim Siddiqui,[...]. Nat Methods 2009
21

A general and flexible method for signal extraction from single-cell RNA-seq data.
Davide Risso, Fanny Perraudeau, Svetlana Gribkova, Sandrine Dudoit, Jean-Philippe Vert. Nat Commun 2018
219
20

Comparative Analysis of Single-Cell RNA Sequencing Methods.
Christoph Ziegenhain, Beate Vieth, Swati Parekh, Björn Reinius, Amy Guillaumet-Adkins, Martha Smets, Heinrich Leonhardt, Holger Heyn, Ines Hellmann, Wolfgang Enard. Mol Cell 2017
601
20

Deep generative modeling for single-cell transcriptomics.
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, Nir Yosef. Nat Methods 2018
305
20

A comparison of single-cell trajectory inference methods.
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, Yvan Saeys. Nat Biotechnol 2019
378
19

Dimensionality reduction for visualizing single-cell data using UMAP.
Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux, Evan W Newell. Nat Biotechnol 2018
18

Full-length RNA-seq from single cells using Smart-seq2.
Simone Picelli, Omid R Faridani, Asa K Björklund, Gösta Winberg, Sven Sagasser, Rickard Sandberg. Nat Protoc 2014
18

Single-cell RNA-seq denoising using a deep count autoencoder.
Gökcen Eraslan, Lukas M Simon, Maria Mircea, Nikola S Mueller, Fabian J Theis. Nat Commun 2019
222
18

Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.
Amit Zeisel, Ana B Muñoz-Manchado, Simone Codeluppi, Peter Lönnerberg, Gioele La Manno, Anna Juréus, Sueli Marques, Hermany Munguba, Liqun He, Christer Betsholtz,[...]. Science 2015
17

Missing data and technical variability in single-cell RNA-sequencing experiments.
Stephanie C Hicks, F William Townes, Mingxiang Teng, Rafael A Irizarry. Biostatistics 2018
166
17

Spatial reconstruction of single-cell gene expression data.
Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, Aviv Regev. Nat Biotechnol 2015
17

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
Laleh Haghverdi, Aaron T L Lun, Michael D Morgan, John C Marioni. Nat Biotechnol 2018
572
17

SCANPY: large-scale single-cell gene expression data analysis.
F Alexander Wolf, Philipp Angerer, Fabian J Theis. Genome Biol 2018
16

Quantitative single-cell RNA-seq with unique molecular identifiers.
Saiful Islam, Amit Zeisel, Simon Joost, Gioele La Manno, Pawel Zajac, Maria Kasper, Peter Lönnerberg, Sten Linnarsson. Nat Methods 2014
643
16


Fast, sensitive and accurate integration of single-cell data with Harmony.
Ilya Korsunsky, Nghia Millard, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-Ru Loh, Soumya Raychaudhuri. Nat Methods 2019
613
16

Computational and analytical challenges in single-cell transcriptomics.
Oliver Stegle, Sarah A Teichmann, John C Marioni. Nat Rev Genet 2015
574
16

Simultaneous epitope and transcriptome measurement in single cells.
Marlon Stoeckius, Christoph Hafemeister, William Stephenson, Brian Houck-Loomis, Pratip K Chattopadhyay, Harold Swerdlow, Rahul Satija, Peter Smibert. Nat Methods 2017
860
16

SCENIC: single-cell regulatory network inference and clustering.
Sara Aibar, Carmen Bravo González-Blas, Thomas Moerman, Vân Anh Huynh-Thu, Hana Imrichova, Gert Hulselmans, Florian Rambow, Jean-Christophe Marine, Pierre Geurts, Jan Aerts,[...]. Nat Methods 2017
844
16

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
Mark D Robinson, Davis J McCarthy, Gordon K Smyth. Bioinformatics 2010
15

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.
Aaron T L Lun, Karsten Bach, John C Marioni. Genome Biol 2016
426
15

Accounting for technical noise in single-cell RNA-seq experiments.
Philip Brennecke, Simon Anders, Jong Kyoung Kim, Aleksandra A Kołodziejczyk, Xiuwei Zhang, Valentina Proserpio, Bianka Baying, Vladimir Benes, Sarah A Teichmann, John C Marioni,[...]. Nat Methods 2013
532
15

Eleven grand challenges in single-cell data science.
David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J McCarthy, Stephanie C Hicks, Mark D Robinson, Catalina A Vallejos, Kieran R Campbell, Niko Beerenwinkel, Ahmed Mahfouz,[...]. Genome Biol 2020
219
15

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications.
Ashraful Haque, Jessica Engel, Sarah A Teichmann, Tapio Lönnberg. Genome Med 2017
266
15

The single-cell transcriptional landscape of mammalian organogenesis.
Junyue Cao, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M Ibrahim, Andrew J Hill, Fan Zhang, Stefan Mundlos, Lena Christiansen, Frank J Steemers,[...]. Nature 2019
631
15

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
Florian Buettner, Kedar N Natarajan, F Paolo Casale, Valentina Proserpio, Antonio Scialdone, Fabian J Theis, Sarah A Teichmann, John C Marioni, Oliver Stegle. Nat Biotechnol 2015
590
14



RNA velocity of single cells.
Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E Kastriti, Peter Lönnerberg, Alessandro Furlan,[...]. Nature 2018
883
14

Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells.
Daniel Ramsköld, Shujun Luo, Yu-Chieh Wang, Robin Li, Qiaolin Deng, Omid R Faridani, Gregory A Daniels, Irina Khrebtukova, Jeanne F Loring, Louise C Laurent,[...]. Nat Biotechnol 2012
872
14

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
Kelly Street, Davide Risso, Russell B Fletcher, Diya Das, John Ngai, Nir Yosef, Elizabeth Purdom, Sandrine Dudoit. BMC Genomics 2018
446
14

DrImpute: imputing dropout events in single cell RNA sequencing data.
Wuming Gong, Il-Youp Kwak, Pruthvi Pota, Naoko Koyano-Nakagawa, Daniel J Garry. BMC Bioinformatics 2018
108
13

Normalizing single-cell RNA sequencing data: challenges and opportunities.
Catalina A Vallejos, Davide Risso, Antonio Scialdone, Sandrine Dudoit, John C Marioni. Nat Methods 2017
181
13

Power analysis of single-cell RNA-sequencing experiments.
Valentine Svensson, Kedar Nath Natarajan, Lam-Ha Ly, Ricardo J Miragaia, Charlotte Labalette, Iain C Macaulay, Ana Cvejic, Sarah A Teichmann. Nat Methods 2017
278
13


Smart-seq2 for sensitive full-length transcriptome profiling in single cells.
Simone Picelli, Åsa K Björklund, Omid R Faridani, Sven Sagasser, Gösta Winberg, Rickard Sandberg. Nat Methods 2013
13


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.